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DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases

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Abstract

Wheat diseases seriously restrict the safety of wheat production and food quality. For farmers and agriculture technicians, diagnosing the disease with the naked eye is not suitable for modern precision agriculture. Deep learning has shown promise in crop disease diagnosis, but accuracy and speed remain a significant challenge in natural field conditions. In this study, a novel DAE-Mask method based on diversification-augmented features and edge features was proposed for intelligent wheat disease detection. DAE-Mask used Densely Connected Convolutional Networks (DenseNet) for preliminary feature extraction, and a backbone feature extraction network combining Feature Pyramid Network (FPN) and attention mechanism was designed to extract diversification-augmented features. To accelerate DAE-Mask, an Edge Agreement Head module based on Sobel filters was designed to compare edge features during training, which improved the model’s mask generation efficiency. We also built a multi-scene wheat disease dataset, MSWDD2022, containing images of wheat stripe rust, wheat powdery mildew, wheat yellow dwarf, and wheat scab. Our model achieved detection speed of 0.08s/pic. On MSWDD2022, our model with mean average precision (mAP) of 96.02% outperformed YOLOv5s, YOLOv8x, SSD, EfficientDet, CenterNet, and RefineDet by 7.79, 1.32, 3.54, 4.79, 9.77, and 5.29 percentage points, respectively. On the public dataset PlantDoc, our model with mAP of 57.68% outperformed YOLOv5s, YOLOv8x, SSD, EfficientDet, CenterNet, and RefineDet by 27.76, 6.48, 14.43, 11.79, 19.40, and 13.40 percentage points, respectively. Finally, the DAE-Mask was deployed on WeChat Mini Program to realize the real-time detection of in-field wheat diseases. The mAP reached 92.78%, and the average return delay of each image was 1.43s.

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Data availability

The relevant image data for this study has been openly shared and can be accessed on the Github platform (https://github.com/NWAFU-YcZhang/Dataset-of-DAE-Mask). We encourage fellow researchers to utilize this data to facilitate academic discourse and collaboration.

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Acknowledgements

The authors express their gratitude to staff of Jingzhuang, Liangma, and Cao Xinzhuang Experimental Bases for their help with data collection. The authors would like to thank Pro. Meili Wang, all editors and anonymous reviewers for their constructive advice.

Funding

This study was supported by China Agriculture Research System of Wheat (CARS-03-37); Key Research and Development Program of Shaanxi Province (2023-YBNY-220); Major Science and Technology Project of Shaanxi Agricultural Collaborative Innovation and Promotion Alliance in 2022 (LMZD202203); Innovation Training Program for College Students in Shaanxi Province (S202110712436).

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  1. Rui Mao and Yuchen Zhang have contributed equally to this work.

Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Yangling, Xianyang, 712100, Shaanxi, China

    Rui Mao, Yuchen Zhang, Zexi Wang, Tao Zhu & Shengchang Gao

  2. College of Plant Protection, Northwest A &F University, Yangling, Xianyang, 712100, Shaanxi, China

    Xingan Hao & Xiaoping Hu

  3. Key Laboratory of Integrated Pest Management on Crops in Northwestern Loess Plateau, Ministry of Agriculture and Rural Affairs, Yangling, Xianyang, 712100, Shaanxi, China

    Xiaoping Hu

  4. Key Laboratory of Plant Protection Resources & Pest Management, Ministry of Education, Yangling, Xianyang, 712100, Shaanxi, China

    Xiaoping Hu

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Supplementary file1 Supplementary Figure Fig. S1 Comparison of detection results using DAE-Mask, YOLOv5s, YOLOv8x, SSD, EfficientDet, CenterNet, and RefineDet models on PlantDoc. Figure S1 shows the test results of the above 7 models on public dataset PlantDoc. From the test effect, the performance of the DAE-Mask model is the best, which is consistent with the performance data in Table 6 (PDF 2507 KB)

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Mao, R., Zhang, Y., Wang, Z. et al. DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases. Precision Agric 25, 785–810 (2024). https://doi.org/10.1007/s11119-023-10093-x

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